text: """Modified Olivetti faces dataset.
  
  The original database was available from (now defunct)
  
      https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
  
  The version retrieved here comes in MATLAB format from the personal
  web page of Sam Roweis:
  
      https://cs.nyu.edu/~roweis/
  """
  
  # Copyright (c) 2011 David Warde-Farley <wardefar at iro dot umontreal dot ca>
  # License: BSD 3 clause
  
  from os.path import dirname, exists, join
  from os import makedirs, remove
  
  import numpy as np
  from scipy.io.matlab import loadmat
  
  from .base import get_data_home
  from .base import _fetch_remote
  from .base import RemoteFileMetadata
  from .base import _pkl_filepath
  from ..utils import _joblib
  from ..utils import check_random_state, Bunch
  
  # The original data can be found at:
  # https://cs.nyu.edu/~roweis/data/olivettifaces.mat
  FACES = RemoteFileMetadata(
      filename='olivettifaces.mat',
      url='https://ndownloader.figshare.com/files/5976027',
      checksum=('b612fb967f2dc77c9c62d3e1266e0c73'
                'd5fca46a4b8906c18e454d41af987794'))
  
  
  def fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0,
                           download_if_missing=True):
      """Load the Olivetti faces data-set from AT&T (classification).
  
      Download it if necessary.
  
      =================   =====================
      Classes                                40
      Samples total                         400
      Dimensionality                       4096
      Features            real, between 0 and 1
      =================   =====================
  
      Read more in the :ref:`User Guide <olivetti_faces_dataset>`.
  
      Parameters
      ----------
      data_home : optional, default: None
          Specify another download and cache folder for the datasets. By default
          all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
  
      shuffle : boolean, optional
          If True the order of the dataset is shuffled to avoid having
          images of the same person grouped.
  
      random_state : int, RandomState instance or None (default=0)
          Determines random number generation for dataset shuffling. Pass an int
          for reproducible output across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      download_if_missing : optional, True by default
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      An object with the following attributes:
  
      data : numpy array of shape (400, 4096)
          Each row corresponds to a ravelled face image of original size
          64 x 64 pixels.
  
      images : numpy array of shape (400, 64, 64)
          Each row is a face image corresponding to one of the 40 subjects
          of the dataset.
  
      target : numpy array of shape (400, )
          Labels associated to each face image. Those labels are ranging from
          0-39 and correspond to the Subject IDs.
  
      DESCR : string
          Description of the modified Olivetti Faces Dataset.
      """
      data_home = get_data_home(data_home=data_home)
      if not exists(data_home):
          makedirs(data_home)
      filepath = _pkl_filepath(data_home, 'olivetti.pkz')
      if not exists(filepath):
          if not download_if_missing:
              raise IOError("Data not found and `download_if_missing` is False")
  
          print('downloading Olivetti faces from %s to %s'
                % (FACES.url, data_home))
          mat_path = _fetch_remote(FACES, dirname=data_home)
          mfile = loadmat(file_name=mat_path)
          # delete raw .mat data
          remove(mat_path)
  
          faces = mfile['faces'].T.copy()
          _joblib.dump(faces, filepath, compress=6)
          del mfile
      else:
          faces = _joblib.load(filepath)
  
      # We want floating point data, but float32 is enough (there is only
      # one byte of precision in the original uint8s anyway)
      faces = np.float32(faces)
      faces = faces - faces.min()
      faces /= faces.max()
      faces = faces.reshape((400, 64, 64)).transpose(0, 2, 1)
      # 10 images per class, 400 images total, each class is contiguous.
      target = np.array([i // 10 for i in range(400)])
      if shuffle:
          random_state = check_random_state(random_state)
          order = random_state.permutation(len(faces))
          faces = faces[order]
          target = target[order]
  
      module_path = dirname(__file__)
      with open(join(module_path, 'descr', 'olivetti_faces.rst')) as rst_file:
          fdescr = rst_file.read()
  
      return Bunch(data=faces.reshape(len(faces), -1),
                   images=faces,
                   target=target,
                   DESCR=fdescr)
  